Heart disease is the leading cause of deaths in the US with aortic valve (AV) diseases being major contributors. Valve replacement is the primary therapeutic indication for AV diseases and transcatheter aortic valve replacement (TAVR) provides a safe and minimally invasive option. However, post-TAVR patient outcomes show considerable variability with deployment parameters. TAVR valves are also susceptible to failure mechanisms like leaflet thrombosis which increase the risk for serious thromboembolic events. Early detection and intervention can avert such outcomes, but symptoms often manifest at advanced stages of valve failure. Continuous monitoring can facilitate early detection, but regulatory and technological challenges may hinder developing such technology through experimental or clinical means.
Computer simulations enable unprecedented predictive capabilities which can help gain insights into the pathophysiology of valvular diseases, conduct in silico trials to design novel monitoring technologies and even guide surgeries for optimal valve deployment. However, accurate, yet efficient numerical models are required. This study describes the implementation of a versatile, efficient AV dynamics model in a previously developed fluid-structure interaction solver, and its application to each of these tasks. The model accelerates simulations by simplifying the constitutive parameter space and equations governing leaflet motion without compromising accuracy. It can simulate native and prosthetic valve dynamics exhibiting physiological and pathological function in idealized and personalized aorta anatomies.
This computational framework is used to generate canonical and patient-specific simulation datasets describing hemodynamic differences secondary to healthy and pathological AVs. These differences help identify biomarkers which reliably predict the risk of valvular and vascular diseases. Changes in these biomarkers are used to assess whether TAVR can deter aortic disease progression. Next, statistical differences in such biomarkers recorded by virtual wearable or embedded sensor systems, between normal and abnormal AV function, are analyzed using data-driven methods to infer valve health. This lays the groundwork for inexpensive, at-home diagnostic technologies, based on digital auscultation and in situ embedded-sensor platforms. Finally, a simulation describing the deployment of a commercially available TAVR valve in a patient-specific aorta anatomy and the associated hemodynamics is presented. Such simulations empower clinicians to optimize TAVR deployment and, consequently, patient outcomes
